A visual AI Agent configuration platform built on LangGraph and LangChain, enabling complex agent workflows through drag-and-drop interface with support for custom nodes and state variable management.
One-Minute Overview#
Lang-Agent is a visual AI Agent configuration platform built on LangGraph, enabling complex agent workflows through a drag-and-drop interface. It's designed for developers who need to build custom AI applications. Unlike traditional workflow tools that only pass outputs from one node to the next, Lang-Agent allows custom state variables that can be used in both nodes and conditional edges for more precise control.
Core Value: Provides a highly customizable AI Agent construction platform that allows building complex business logic without深入 LangGraph implementation details.
Quick Start#
Installation Difficulty: Medium - Requires separate frontend and backend setup with some technical prerequisites
# Clone the repository
git clone https://github.com/cqzyys/lang-agent.git
# Install backend
cd lang-agent-backend
poetry env use python
poetry shell
poetry install
# Install frontend
cd lang-agent-frontend
yarn install
Is this suitable for me?
- ✅ Complex AI Agent Development: When you need to configure multi-step AI workflows visually
- ✅ Enterprise AI Applications: When you need to integrate multiple models and external tools
- ❌ Simple Chatbots: When you only need basic LLM functionality
- ❌ No Programming Experience: When you need a no-code solution for AI applications
Core Capabilities#
1. Visual Agent Configuration - Simplifying Complex Workflow Construction#
Build complex AI Agent workflows through drag-and-drop nodes and edges without writing complex LangGraph code. User Value: Lowers technical barriers, allowing developers to focus on business logic rather than underlying implementation.
2. Custom Node System - Flexible Extension Capabilities#
Developers can easily create custom nodes to implement specific business logic without modifying core code. User Value: Enables customization based on business requirements to meet specific scenario needs.
3. State Variable Management - Precise Process Control#
Supports custom state variables that can be used in nodes and conditional edges for more precise workflow control. User Value: Implements complex interaction logic and data flow, enhancing the intelligence level of Agents.
Tech Stack & Integration#
Development Languages: Python, TypeScript, JavaScript Main Dependencies: LangGraph, FastAPI, React, ReactFlow, HeroUI, Tailwind CSS Integration Method: Complete frontend-backend architecture with API communication
Ecosystem & Extensions#
- Custom Nodes: Project provides comprehensive custom node development guides supporting both frontend React components and backend Python logic implementation
- Model Support: Supports LLM, VLM, and Embedding models, compatible with OpenAI interface channels
- Database Integration: Supports PostgreSQL and Milvus vector databases
- Tool Integration: Integrates external tools and services through MCP protocol
Maintenance Status#
- Development Activity: Actively maintained with clear version iteration plans
- Recent Updates: Project continues to receive commits and updates
- Community Response: Open source project where community can contribute and provide feedback through GitHub
Commercial & License#
License: Apache-2.0
- ✅ Commercial Use: Permitted
- ✅ Modification: Permitted
- ⚠️ Restrictions: Must include license and copyright notices
Documentation & Learning Resources#
- Documentation Quality: Comprehensive, including detailed installation and usage instructions
- Official Documentation: https://github.com/cqzyys/lang-agent
- Example Code: Provides multiple use case examples demonstrating different types of Agent configurations